Host response-based diagnostics for identifying bacterial versus viral causes of lower respiratory infection in resource-limited settings

基于宿主反应的诊断，用于识别资源有限环境中下呼吸道感染的细菌与病毒原因

• 批准号: 10452456
• 负责人: GAYANI TILLEKERATNE
• 金额: $ 28.36万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10452456
• 关键词: Adult; Anti-Bacterial Agents; Antimicrobial Resistance; Area; Asian population; Bacterial Infections; Biological; Biological Assay; Biological Markers; Blood; Blood specimen; COVID-19; COVID-19 pandemic; COVID-19 patient; Characteristics; Childhood; Clinical; Collection; Consensus; Country; Custom; Data; Detection; Diagnosis; Diagnostic; Enrollment; Epithelial Cells; Etiology; Fever; Funding; Gene Expression; Genes; Genomic medicine; Goals; Immune response; Income; Infection; Infrastructure; Knowledge; Laboratories; Leukocytes; Logistic Regressions; Lower Respiratory Tract Infection; Machine Learning; Microbiology; Minority; Molecular; Nasal Epithelium; Nasopharynx; Outcome; Patients; Performance; Polymerase Chain Reaction; Population; Research; Research Infrastructure; Resource-limited setting; Resources; Sampling; South Asian; Sputum; Sri Lanka; Testing; Time; Translating; Viral; Virus Diseases; Work; adjudicate; adjudication; base; biobank; clinical diagnostics; cohort; combat; density; diagnostic platform; improved; migration; novel; pathogen; pathogenic bacteria; pathogenic virus; peripheral blood; point of care; precision medicine; procalcitonin; prospective; respiratory; transcriptome sequencing

Project Summary/ Abstract Lower respiratory tract infection (LRTI) is a common reason for antibacterial use and misuse globally. Limitations associated with current LRTI diagnostics are a major driver of antibacterial overuse. Pathogen- based diagnostics have limited sensitivity and do not distinguish infection from colonization. In low- or middle- income countries (LMICs), LRTI diagnosis is further hindered by limited laboratory infrastructure. Host-based diagnostics that leverage the host’s response to infection and broadly classify infection as viral or bacterial in etiology could greatly reduce inappropriate antibacterial use for LRTI. Previously, we showed that novel, peripheral blood-based gene expression classifiers accurately identified bacterial versus viral febrile respiratory illness in a South Asian population. While promising, these classifiers require the collection of a blood sample, which may be challenging in pediatric populations or in LMIC settings with limited resources. Emerging data suggest that the host response in the nasopharynx may also help identify class of infection. Nasopharyngeal sampling offers the possibility of an integrated diagnostic that combines both pathogen and host response detection in a single sample, which would be especially attractive in LMIC settings. The objective of this application is to determine the performance characteristics of NP-based gene expression classifiers at differentiating viral versus bacterial LRTI in a South Asian population. The following aims are proposed 1) to derive NP-based gene expression classifiers to discriminate viral versus bacterial LRTI, and 2) to transfer the NP-based classifier to a real-time polymerase chain reaction (RT-PCR) assay that has potential to be translated to a clinical platform. Comprehensive microbiological and molecular testing for respiratory viral and bacterial pathogens will be completed. Subjects will be adjudicated as having viral versus bacterial LRTI, and RNA sequencing will be performed using NP samples. Machine-learning approaches will identify host gene expression classifiers that discriminate viral versus bacterial LRTI. The genes identified in the NP-based classifier will be migrated onto customized, TaqMan Low-Density Array (TLDA) cards and RT-PCR will be performed. Gene expression will be quantified and logistic regression performed to identify viral versus bacterial LRTI. The expected outcome of this proposal is a significant improvement in our knowledge of how novel NP-based gene expression classifiers perform at identifying viral versus bacterial LRTI in a South Asian population. Following successful completion of these aims, we plan to translate the NP-based classifier to a point-of-care, clinical diagnostic platform. The long-term goal of this work is to develop strategies for improving antibacterial use in LMICs and to help combat the global crisis of antimicrobial resistance.

项目总结/摘要 下呼吸道感染（LRTI）是全球抗菌药物使用和滥用的常见原因。 与当前LRTI诊断相关的局限性是抗菌药物过度使用的主要驱动因素。病原体- 基于诊断的诊断具有有限的灵敏度，并且不能区分感染和定殖。在低-或中等- 在低收入国家（LMIC），LRTI诊断进一步受到有限的实验室基础设施的阻碍。基于主机 利用宿主对感染的反应并将感染大致分类为病毒或细菌的诊断， 病原学可以大大减少下呼吸道感染的不适当抗菌药物使用。之前我们展示了这部小说 基于外周血的基因表达分类器准确地识别了细菌与病毒性呼吸道发热 南亚人的疾病。虽然很有前途，但这些分类器需要收集血液样本， 这在儿科人群或资源有限的LMIC环境中可能是具有挑战性的。新出现的数据 表明鼻咽部的宿主反应也可能有助于识别感染的类别。鼻咽 采样提供了结合病原体和宿主反应的综合诊断的可能性 在单个样品中进行检测，这在LMIC设置中特别有吸引力。的目的 应用是确定基于NP的基因表达分类器的性能特征， 区分南亚人群中的病毒性与细菌性LRTI。提出了以下目标：1） 推导出基于NP的基因表达分类器，以区分病毒与细菌LRTI，以及2）将LRTI转移到 基于NP的分类器到实时聚合酶链反应（RT-PCR）测定，其具有潜在的 转化为临床平台。全面的微生物和分子检测呼吸道病毒和 细菌病原体将完成。受试者将被裁定为患有病毒性LRTI与细菌性LRTI，并且 将使用NP样本进行RNA测序。机器学习方法将识别宿主基因 区分病毒与细菌LRTI的表达分类器。基于NP的基因识别 分类器将被迁移到定制的TaqMan低密度阵列（TLDA）卡上，RT-PCR将被 执行。将对基因表达进行定量，并进行逻辑回归，以确定病毒与 细菌LRTI。这一建议的预期结果是大大提高我们对如何做到这一点的认识。 一种新的基于NP的基因表达分类器在南亚人中识别病毒与细菌LRTI 人口在成功完成这些目标之后，我们计划将基于NP的分类器转换为 即时临床诊断平台。这项工作的长期目标是制定战略， 在LMIC中使用抗菌药物，并帮助应对全球抗菌药物耐药性危机。